# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.

from concurrent.futures import ThreadPoolExecutor
import time

import cupy as cp
import numpy as np
import netifaces as ni
import pytest

from mscclpp import Fifo, Host2DeviceSemaphore, Host2HostSemaphore, ProxyService, SmDevice2DeviceSemaphore, Transport
from ._cpp import _ext
from .mscclpp_group import MscclppGroup
from .mscclpp_mpi import MpiGroup, parametrize_mpi_groups, mpi_group
from .utils import KernelBuilder, pack

ethernet_interface_name = "eth0"


def all_ranks_on_the_same_node(mpi_group: MpiGroup):
    if (ethernet_interface_name in ni.interfaces()) is False:
        pytest.skip(f"{ethernet_interface_name} is not an interface to use on this node")
    my_ip = ni.ifaddresses(ethernet_interface_name)[ni.AF_INET][0]["addr"]
    root_ip = mpi_group.comm.bcast(my_ip, 0)
    last_rank_ip = mpi_group.comm.bcast(my_ip, mpi_group.comm.size - 1)
    return last_rank_ip == root_ip


@parametrize_mpi_groups(2, 4, 8, 16)
@pytest.mark.parametrize("ifIpPortTrio", ["eth0:localhost:50000", ethernet_interface_name, ""])
def test_group_with_ip(mpi_group: MpiGroup, ifIpPortTrio: str):
    if (ethernet_interface_name in ni.interfaces()) is False:
        pytest.skip(f"{ethernet_interface_name} is not an interface to use on this node")
    my_ip = ni.ifaddresses(ethernet_interface_name)[ni.AF_INET][0]["addr"]
    root_ip = mpi_group.comm.bcast(my_ip, 0)
    if ifIpPortTrio == ethernet_interface_name:
        ifIpPortTrio += ":" + root_ip + ":50000"  # some random port

    if all_ranks_on_the_same_node(mpi_group) is False and "localhost" in ifIpPortTrio:
        # ranks are on different nodes
        pytest.skip("this case is not supported as localhost will be different for different nodes")

    group = MscclppGroup(mpi_group, ifIpPortTrio)

    nelem = 1024
    memory = np.zeros(nelem, dtype=np.int32)
    nelemPerRank = nelem // group.nranks
    memory[(nelemPerRank * group.my_rank) : (nelemPerRank * (group.my_rank + 1))] = group.my_rank + 1
    memory_expected = np.zeros_like(memory)
    for rank in range(group.nranks):
        memory_expected[(nelemPerRank * rank) : (nelemPerRank * (rank + 1))] = rank + 1

    for rank in range(group.nranks):
        if rank == group.my_rank:
            continue
        group.send(memory[(nelemPerRank * group.my_rank) : (nelemPerRank * (group.my_rank + 1))], rank, 0)
    for rank in range(group.nranks):
        if rank == group.my_rank:
            continue
        group.recv(memory[(nelemPerRank * rank) : (nelemPerRank * (rank + 1))], rank, 0)

    assert np.array_equal(memory, memory_expected)


def create_and_connect(mpi_group: MpiGroup, transport: str):
    if transport == "NVLink" and all_ranks_on_the_same_node(mpi_group) is False:
        pytest.skip("cannot use nvlink for cross node")
    group = MscclppGroup(mpi_group)

    remote_nghrs = list(range(mpi_group.comm.size))
    remote_nghrs.remove(mpi_group.comm.rank)
    if transport == "NVLink":
        tran = Transport.CudaIpc
    elif transport == "IB":
        tran = group.my_ib_device(group.my_rank % 8)
    else:
        assert False
    connections = group.make_connection(remote_nghrs, tran)
    return group, connections


@parametrize_mpi_groups(2, 4, 8, 16)
@pytest.mark.parametrize("transport", ["IB", "NVLink"])
def test_group_with_connections(mpi_group: MpiGroup, transport: str):
    create_and_connect(mpi_group, transport)


@parametrize_mpi_groups(2, 4, 8, 16)
@pytest.mark.parametrize("transport", ["IB", "NVLink"])
@pytest.mark.parametrize("nelem", [2**i for i in [10, 15, 20]])
def test_connection_write(mpi_group: MpiGroup, transport: Transport, nelem: int):
    group, connections = create_and_connect(mpi_group, transport)
    memory = cp.zeros(nelem, dtype=cp.int32)
    nelemPerRank = nelem // group.nranks
    sizePerRank = nelemPerRank * memory.itemsize
    memory[(nelemPerRank * group.my_rank) : (nelemPerRank * (group.my_rank + 1))] = group.my_rank + 1
    memory_expected = cp.zeros_like(memory)
    for rank in range(group.nranks):
        memory_expected[(nelemPerRank * rank) : (nelemPerRank * (rank + 1))] = rank + 1
    group.barrier()
    all_reg_memories = group.register_tensor_with_connections(memory, connections)
    for rank in connections:
        connections[rank].write(
            all_reg_memories[rank],
            sizePerRank * group.my_rank,
            all_reg_memories[group.my_rank],
            sizePerRank * group.my_rank,
            sizePerRank,
        )
    poll_for = 100
    for i in range(poll_for):
        all_correct = cp.array_equal(memory, memory_expected)
        if all_correct:
            break
        time.sleep(0.1)
    for conn in connections:
        connections[conn].flush()
    cp.cuda.runtime.deviceSynchronize()
    group.barrier()
    assert all_correct


@parametrize_mpi_groups(2, 4, 8, 16)
@pytest.mark.parametrize("transport", ["IB", "NVLink"])
@pytest.mark.parametrize("nelem", [2**i for i in [10, 15, 20, 27]])
@pytest.mark.parametrize("device", ["cuda", "cpu"])
def test_connection_write_and_signal(mpi_group: MpiGroup, transport: Transport, nelem: int, device: str):
    # this test starts with a random tensor on rank 0 and rotates it all the way through all ranks
    # and finally, comes back to rank 0 to make sure it matches all the original values

    if device == "cpu" and transport == "NVLink":
        pytest.skip("nvlink doesn't work with host allocated memory")
    group, connections = create_and_connect(mpi_group, transport)
    xp = cp if device == "cuda" else np
    if group.my_rank == 0:
        memory = xp.random.randn(nelem)
        memory = memory.astype(xp.float32)
        memory_expected = memory.copy()
    else:
        memory = xp.zeros(nelem, dtype=xp.float32)
    if device == "cuda":
        cp.cuda.runtime.deviceSynchronize()

    signal_memory = xp.zeros(1, dtype=xp.int64)
    all_reg_memories = group.register_tensor_with_connections(memory, connections)
    all_signal_memories = group.register_tensor_with_connections(signal_memory, connections)

    next_rank = (group.my_rank + 1) % group.nranks
    bufferSize = nelem * memory.itemsize
    dummy_memory_on_cpu = np.zeros(1, dtype=np.int64)

    signal_val = 123
    if group.my_rank != 0:
        while signal_memory[0] != signal_val:
            time.sleep(0.1)
    connections[next_rank].write(all_reg_memories[next_rank], 0, all_reg_memories[group.my_rank], 0, bufferSize)
    connections[next_rank].flush()
    if group.my_rank == 0:
        memory[:] = 0
        if device == "cuda":
            cp.cuda.runtime.deviceSynchronize()
    connections[next_rank].update_and_sync(
        all_signal_memories[next_rank], 0, dummy_memory_on_cpu.ctypes.data, signal_val
    )
    all_correct = False
    if group.my_rank == 0:
        while signal_memory[0] != signal_val:
            time.sleep(0.1)
        all_correct = cp.array_equal(memory, memory_expected)
    group.barrier()
    all_correct = mpi_group.comm.bcast(all_correct, 0)
    assert all_correct


@parametrize_mpi_groups(2, 4, 8, 16)
def test_h2h_semaphores(mpi_group: MpiGroup):
    group, connections = create_and_connect(mpi_group, "IB")

    semaphores = group.make_semaphore(connections, Host2HostSemaphore)
    for rank in connections:
        semaphores[rank].signal()

    for rank in connections:
        semaphores[rank].wait()
    group.barrier()


class MscclppKernel:
    def __init__(
        self,
        test_name,
        my_rank=None,
        nranks=None,
        semaphore_or_channels=None,
        tensor=None,
        use_packet=False,
        scratch=None,
        fifo=None,
    ):
        if test_name == "h2d_semaphore":
            self._kernel = KernelBuilder(
                file="h2d_semaphore_test.cu", kernel_name="h2d_semaphore"
            ).get_compiled_kernel()
            self.nblocks = 1
            self.nthreads = nranks
        elif test_name == "d2d_semaphore":
            self._kernel = KernelBuilder(
                file="d2d_semaphore_test.cu", kernel_name="d2d_semaphore"
            ).get_compiled_kernel()
            self.nblocks = 1
            self.nthreads = nranks
        elif test_name == "sm_channel":
            self._kernel = KernelBuilder(file="sm_channel_test.cu", kernel_name="sm_channel").get_compiled_kernel()
            self.nblocks = nranks
            self.nthreads = 1024
        elif test_name == "fifo":
            self._kernel = KernelBuilder(file="fifo_test.cu", kernel_name="fifo").get_compiled_kernel()
            self.nblocks = 1
            self.nthreads = 1
        elif test_name == "proxy":
            self._kernel = KernelBuilder(file="proxy_test.cu", kernel_name="proxy").get_compiled_kernel()
            self.nblocks = 1
            self.nthreads = nranks
        elif test_name == "simple_proxy_channel":
            self._kernel = KernelBuilder(
                file="simple_proxy_channel_test.cu", kernel_name="simple_proxy_channel"
            ).get_compiled_kernel()
            self.nblocks = 1
            self.nthreads = 1024
        else:
            assert False

        self.params = b""
        if test_name in ["h2d_semaphore", "d2d_semaphore", "sm_channel", "simple_proxy_channel"]:
            first_arg = next(iter(semaphore_or_channels.values()))
            size_of_semaphore_or_channels = len(first_arg.device_handle().raw)
            device_handles = []
            for rank in range(nranks):
                if rank == my_rank:
                    device_handles.append(
                        bytes(size_of_semaphore_or_channels)
                    )  # just zeros for semaphores that do not exist
                else:
                    device_handles.append(semaphore_or_channels[rank].device_handle().raw)
            # keep a reference to the device handles so that they don't get garbage collected
            self._d_semaphore_or_channels = cp.asarray(memoryview(b"".join(device_handles)), dtype=cp.uint8)
            self.params += pack(self._d_semaphore_or_channels, my_rank, nranks)
            if test_name == "sm_channel":
                self.params += pack(tensor.size, use_packet)
            if test_name == "simple_proxy_channel":
                self.params += pack(tensor, scratch, tensor.size, use_packet)
        elif test_name == "fifo":
            self.params = fifo.device_handle().raw
        elif test_name == "proxy":
            semaphore_device_handles = [semaphore.device_handle().raw for semaphore in semaphore_or_channels]
            self._d_semaphore_or_channels = cp.asarray(memoryview(b"".join(semaphore_device_handles)), dtype=cp.uint8)
            self.params = pack(my_rank, nranks) + fifo.raw + pack(self._d_semaphore_or_channels)

    def __call__(self):
        return self._kernel.launch_kernel(self.params, self.nblocks, self.nthreads, 0, None)


@parametrize_mpi_groups(2, 4, 8, 16)
@pytest.mark.parametrize("transport", ["NVLink", "IB"])
def test_h2d_semaphores(mpi_group: MpiGroup, transport: str):
    def signal(semaphores):
        for rank in semaphores:
            semaphores[rank].signal()

    group, connections = create_and_connect(mpi_group, transport)

    semaphores = group.make_semaphore(connections, Host2DeviceSemaphore)
    kernel = MscclppKernel("h2d_semaphore", group.my_rank, group.nranks, semaphores)
    kernel()

    # workaround: use a separate thread to to let cudaMemcpyAsync run concurrently with the kernel
    with ThreadPoolExecutor(max_workers=1) as executor:
        executor.submit(signal, semaphores)

    cp.cuda.runtime.deviceSynchronize()
    group.barrier()


@parametrize_mpi_groups(2, 4, 8, 16)
def test_d2d_semaphores(mpi_group: MpiGroup):
    group, connections = create_and_connect(mpi_group, "NVLink")

    semaphores = group.make_semaphore(connections, SmDevice2DeviceSemaphore)
    group.barrier()
    kernel = MscclppKernel("d2d_semaphore", group.my_rank, group.nranks, semaphores)
    kernel()
    cp.cuda.runtime.deviceSynchronize()
    group.barrier()


@parametrize_mpi_groups(2, 4, 8, 16)
@pytest.mark.parametrize("nelem", [2**i for i in [10, 15, 20]])
@pytest.mark.parametrize("use_packet", [False, True])
def test_sm_channels(mpi_group: MpiGroup, nelem: int, use_packet: bool):
    group, connections = create_and_connect(mpi_group, "NVLink")

    memory = cp.zeros(nelem, dtype=cp.int32)
    if use_packet:
        scratch = cp.zeros(nelem * 2, dtype=cp.int32)
    else:
        scratch = None
    nelemPerRank = nelem // group.nranks
    nelemPerRank * memory.itemsize
    memory[(nelemPerRank * group.my_rank) : (nelemPerRank * (group.my_rank + 1))] = group.my_rank + 1
    memory_expected = cp.zeros_like(memory)
    for rank in range(group.nranks):
        memory_expected[(nelemPerRank * rank) : (nelemPerRank * (rank + 1))] = rank + 1

    if use_packet:
        channels = group.make_sm_channels_with_packet(memory, scratch, connections)
    else:
        channels = group.make_sm_channels(memory, connections)
    kernel = MscclppKernel("sm_channel", group.my_rank, group.nranks, channels, memory, use_packet, scratch)

    group.barrier()
    kernel()
    cp.cuda.runtime.deviceSynchronize()
    group.barrier()
    assert cp.array_equal(memory, memory_expected)


@parametrize_mpi_groups(2, 4, 8, 16)
def test_fifo(
    mpi_group: MpiGroup,
):
    fifo = Fifo()
    kernel = MscclppKernel("fifo", fifo=fifo)

    kernel()
    poll_for = 100
    for _ in range(poll_for):
        trigger = fifo.poll()
        if trigger.fst == 123:
            return
        time.sleep(0.1)
    assert False


@parametrize_mpi_groups(2, 4, 8, 16)
@pytest.mark.parametrize("nelem", [2**i for i in [10, 15, 20]])
@pytest.mark.parametrize("transport", ["IB", "NVLink"])
def test_proxy(mpi_group: MpiGroup, nelem: int, transport: str):
    group, connections = create_and_connect(mpi_group, transport)

    memory = cp.zeros(nelem, dtype=cp.int32)
    nelemPerRank = nelem // group.nranks
    nelemPerRank * memory.itemsize
    memory[(nelemPerRank * group.my_rank) : (nelemPerRank * (group.my_rank + 1))] = group.my_rank + 1
    memory_expected = cp.zeros_like(memory)
    for rank in range(group.nranks):
        memory_expected[(nelemPerRank * rank) : (nelemPerRank * (rank + 1))] = rank + 1
    group.barrier()
    all_reg_memories = group.register_tensor_with_connections(memory, connections)

    semaphores = group.make_semaphore(connections, Host2DeviceSemaphore)

    list_conn = []
    list_sem = []
    list_reg_mem = []
    first_conn = next(iter(connections.values()))
    first_sem = next(iter(semaphores.values()))
    for rank in range(group.nranks):
        if rank in connections:
            list_conn.append(connections[rank])
            list_sem.append(semaphores[rank])
        else:
            list_conn.append(first_conn)  # just for simplicity of indexing
            list_sem.append(first_sem)

        list_reg_mem.append(all_reg_memories[rank])

    proxy = _ext.MyProxyService(group.my_rank, group.nranks, nelem * memory.itemsize, list_conn, list_reg_mem, list_sem)

    fifo_device_handle = proxy.fifo_device_handle()

    kernel = MscclppKernel(
        "proxy", my_rank=group.my_rank, nranks=group.nranks, semaphore_or_channels=list_sem, fifo=fifo_device_handle
    )
    proxy.start()
    group.barrier()
    kernel()
    cp.cuda.runtime.deviceSynchronize()
    proxy.stop()
    group.barrier()
    assert cp.array_equal(memory, memory_expected)


@parametrize_mpi_groups(2, 4, 8, 16)
@pytest.mark.parametrize("nelem", [2**i for i in [10, 15, 20]])
@pytest.mark.parametrize("transport", ["NVLink", "IB"])
@pytest.mark.parametrize("use_packet", [False, True])
def test_simple_proxy_channel(mpi_group: MpiGroup, nelem: int, transport: str, use_packet: bool):
    group, connections = create_and_connect(mpi_group, transport)

    memory = cp.zeros(nelem, dtype=cp.int32)
    if use_packet:
        scratch = cp.zeros(nelem * 2, dtype=cp.int32)
    else:
        scratch = cp.zeros(1, dtype=cp.int32)  # just so that we can pass a valid ptr
    nelemPerRank = nelem // group.nranks
    nelemPerRank * memory.itemsize
    memory[(nelemPerRank * group.my_rank) : (nelemPerRank * (group.my_rank + 1))] = group.my_rank + 1
    memory_expected = cp.zeros_like(memory)
    for rank in range(group.nranks):
        memory_expected[(nelemPerRank * rank) : (nelemPerRank * (rank + 1))] = rank + 1
    group.barrier()

    proxy_service = ProxyService()
    if use_packet:
        memory_to_register = scratch
    else:
        memory_to_register = memory
    simple_channels = group.make_proxy_channels_with_packet(proxy_service, memory_to_register, connections)

    kernel = MscclppKernel(
        "simple_proxy_channel",
        my_rank=group.my_rank,
        nranks=group.nranks,
        semaphore_or_channels=simple_channels,
        tensor=memory,
        use_packet=use_packet,
        scratch=scratch,
    )
    proxy_service.start_proxy()
    group.barrier()
    kernel()
    cp.cuda.runtime.deviceSynchronize()
    proxy_service.stop_proxy()
    group.barrier()
    assert cp.array_equal(memory, memory_expected)